Silver nanowire networks shown to learn and remember like the human brain: ScienceAlert

Over the past year or so, generative AI models such as ChatGPT and DALL-E have made it possible to produce huge amounts of seemingly human-like, high-quality creative content from a simple series of prompts.

Despite being highly capable — far outperforming humans at big data pattern recognition tasks in particular — current AI systems are not as smart as we are. AI systems are not structured like our brains and they don’t learn in the same way.

Artificial intelligence systems are also used wide Loads of energy and resources for training (compared to our meals of three or more meals a day). Their ability to adapt and function in dynamic, unpredictable, and noisy environments is poor compared to our own, and they also lack human-like memory capabilities.

Our research explores non-biological systems that are similar to human brains. in New study Posted in Science advanceswe found self-organizing networks of tiny silver wires that seem to learn and remember in the same way as the thinking organs in our heads.

brain imitation

Our work is part of a research field called neuroscience, which aims to replicate the structure and functions of neurons and biological synapses in non-biological systems.

Our research focuses on a system that uses a network of “nanowires” to mimic neurons and synapses in the brain.

These nanowires are tiny wires about one-thousandth the width of a human hair. They are made of a highly conductive metal, such as silver, and are usually coated with an insulating material such as plastic.

The nanowires self-assemble to form a network structure similar to a biological neural network. Like neurons that have an insulating membrane, each metallic nanowire is coated with a thin insulating layer.

When we stimulate the nanowires with electrical signals, the ions travel through the insulating layer to an adjacent nanowire (much like neurotransmitters cross a synapse). As a result, we observe synapse-like electrical signals in the nanowire networks.

Learning and memory

Our new work uses this nanowire system to explore the question of human-like intelligence. Two features central to our investigation are indicative of higher-level cognitive function: learning and memory.

Our study shows that we can selectively strengthen (and weaken) synaptic pathways in nanowire networks. This is similar toSupervised learningin the brain.

In this process, the output of the clamps is compared to the desired result. The synapses are then either strengthened (if their output is close to the desired outcome) or pruned (if their output is not close to the desired outcome).

We have expanded on this finding by showing that we can increase the amount of reinforcement by ‘rewarding’ or ‘punishing’ the network. This process is inspired byLearning reinforcementin the brain.

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We also implemented a version of the test called “return task n“which is used to measure working memory in humans. It involves presenting a series of stimuli and comparing each new input with one that occurred a number (n) steps ago.

The network “remembered” the previous signals for at least seven steps. Oddly enough, the number seven is often seen as a average number of items Humans can hold a working memory at one time.

When we use reinforcement learning, we see significant improvements in network memory performance.

In our nanowire networks, we have found that the formation of synaptic pathways depends on how these synapses were activated in the past. This is also the case for synapses in the brain, as neuroscientists call them “Metamorphosis“.

artificial intelligence

Human intelligence is probably still far from being replicated.

However, our research on neural nanowire networks shows that it is possible to implement fundamental features of intelligence—such as learning and memory—in physical, non-biological devices.

Nanowire networks are different from the artificial neural networks used in artificial intelligence. However, it may lead to so-called “artificial intelligence”.

Perhaps a neural-shaped nanowire network could someday learn to carry out more human-like conversations than ChatGPT, and remember them.

Alon Loefflerdoctoral researcher, Sydney University And Zdenka Koncicprofessor of physics, Sydney University

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